{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/online-optimization-with-costly-and-noisy","title":"Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions","arxiv_id":"1603.09620","date":"2016-03-31","proceeding":null,"authors":["Laurens Bliek","Hans R. G. W. Verstraete","Michel Verhaegen","Sander Wahls"],"abstract":"This paper analyzes DONE, an online optimization algorithm that iteratively\nminimizes an unknown function based on costly and noisy measurements. The\nalgorithm maintains a surrogate of the unknown function in the form of a random\nFourier expansion (RFE). The surrogate is updated whenever a new measurement is\navailable, and then used to determine the next measurement point. The algorithm\nis comparable to Bayesian optimization algorithms, but its computational\ncomplexity per iteration does not depend on the number of measurements. We\nderive several theoretical results that provide insight on how the\nhyper-parameters of the algorithm should be chosen. The algorithm is compared\nto a Bayesian optimization algorithm for a benchmark problem and three\napplications, namely, optical coherence tomography, optical beam-forming\nnetwork tuning, and robot arm control. It is found that the DONE algorithm is\nsignificantly faster than Bayesian optimization in the discussed problems,\nwhile achieving a similar or better performance.","url_abs":"http://arxiv.org/abs/1603.09620v3","url_pdf":"http://arxiv.org/pdf/1603.09620v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"online-optimization-with-costly-and-noisy","repo_url":"https://bitbucket.org/csi-dcsc/donecpp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}